Wonen & Energie Hackathon

To merge CBS StatLine datasets across Dutch regional levels, use built-in join recipes that handle formatting issues like trailing spaces in CBS region codes, enabling accurate data synthesis across gemeente, wijk, and buurt boundaries.

Analyzing Dutch housing and energy data via CBS OData is best handled through curated table registries and journalistic analysis recipes, which provide direct access to vetted CBS StatLine tables for trends like gas consumption decline and heat pump adoption.

Yes, you need pandas and cbsodata to execute API-driven data retrieval and synthesis tasks for Dutch regional energy statistics. The cbsodata library handles StatLine API calls while pandas manages the subsequent data cleaning and integration.

Joining CBS StatLine tables results in missing regional data matches due to CBS-specific formatting issues like trailing spaces in region codes. Applying necessary data cleaning steps before merging gemeente, wijk, or buurt datasets resolves these join failures.

Yes, you can visualize CBS housing data joins using folium and plotly. These dependencies support mapping and charting the integrated regional datasets after applying the required CBS region code cleaning and joining recipes.